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The size and development of the shadow economy:

An empirical investigation from states of IndiaB

Kausik Chaudhuri a,*, Friedrich Schneiderb,1, Sumana Chattopadhyay c

a IGIDR, Mumbai, Indiab Department of Economics, Johannes Kepler, University of Linz, Altenbergerstrasse 69, A-4040 Linz-Auhof, Austria

c University of Missouri, Columbia, USA

Received 2 January 2003; accepted 1 February 2005

Abstract

Using the state level data from India, this paper investigates the size of the hidden economy in Indian

states over the period 1974/75 to 1995/96. Our analysis has shown that after liberalization of the Indian

economy in 1991/92, the growth in the size of the hidden economy has decreased on an average. Our results

show that the growth in the size of the hidden economy is approximately 4% less in scheduled election

years than in all other years. We also demonstrate that the growth is significantly lower in those states where

the coalition government is in power. An increased growth of newspapers and the literacy rates translate to

cleaner governance, e.g. to fewer amounts of shadow economy activities in the economy.

D 2005 Elsevier B.V. All rights reserved.

JEL classification: O17; D78; Hll; H26

Keywords: Shadow economy of Indian states; Tax burden; Governance; Political economy of the shadow economy

1. Introduction

Economists and social scientists have shown considerable interest in recent years to measure

the gap between the observable and the actual. This has led to the conceptualization of the

0304-3878/$ - see front matterD 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.jdeveco.2005.02.011

B We would like to thank an anonymous referee for suggesting changes that greatly improved the quality of the paper.

Thanks are also due to Prof. Mark Rosenzweig, The Editor of the journal.

* Corresponding author.

E-mail addresses:[email protected] (K. Chaudhuri), [email protected] (F. Schneider).URL: http://www.econ.iku.at/Schneider/ (F. Schneider).

1 Tel.: +43 70 2468 8210; fax: +43 70 2468 8209.

Journal of Development Economics 80 (2006) 428443

www.elsevier.com/locate/econbase


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dhidden economyT, although several synonyms such as black, shadow, underground, unobserved,

unofficial, unrecorded, and parallel were used for the dhidden economyT. In general, it tries to

capture the activities beyond measurement by official activity.2 The hidden economy consists of

legal and illegal activities outside the reach of the government.

3

Empirical estimates demonstratethat underground activities have been on the rise since the 1970s when the presence of

government activity became stronger in the economies around the world. With increase in tax

rates to finance larger public spending programs, the desire to escape taxes and regulatory

restrictions also gained in prominence (Tanzi and Schuknecht, 1997). Popular print-media

articles were also ready to accept the notion that the underground economy had increased

significantly over the years. Given such media attention, the nexus of the black economy into the

public glare has created a consciousness about the gravity of the phenomenon all over the world.

Given the importance of the phenomenon, the next question that naturally arises is regarding

the definition of the hidden economy. Tanzi (1999) suggests that the shadow economy crops up

because of presence of activities that are difficult to measure and tax, like household work andalso criminal and illegal activities. Schneider (1986) sums this point by defining the underground

economy as ball economic activities that contribute to the value added and should be included in

national income in terms of national accounting conventions but are presently not registered by

national measurement agenciesQ. Bhattacharyya (1999) describes the hidden economy as

reflected by the unrecorded national income bcalculated as the difference between the potential

national income for the given currency in circulation and the recorded national incomeQ.

Bagachwa and Naho (1995) consider it as a combination of informal (small-scale production and

distribution units), parallel (illegal production of legal activities) and black market activities

(production and distribution of market and non-market goods forbidden by the government).

Acharya (1985), in the Indian context, refers to the black economy as bthe aggregate of incomeswhich are taxable but are not reported to the tax authorities Q and also bthe extent to which

estimates of national income and output are biased downwards because of deliberate, false

reporting of incomes, output and transactions for reasons of tax evasion, flouting of other

economic controls and related motivesQ. A commonly used working definition is: all currently

unregistered economic activities, which contribute to the officially calculated (or observed)

Gross National Product.4 Smith (1985, p. 18) defines it as bmarket-based production of goods

and services whether legal or illegal that escapes detection in the official estimates of GDP Q.

The above discussion suggests that the shadow or hidden economy deals with the portion of

the income earned from legal and illegal activities that cannot be accounted for by the standard

measurement procedures used in compilation of national income accounts. We, in this paper,adopt this as a relatively broad definition of the dunderground economyT.

In this paper, we try to estimate the size of the hidden (shadow) economy for fourteen major

states of India over the period 1974/75 to 1995/96 using a multiple indicator multiple cause

(MIMIC) model. Given the estimates of the size of the shadow economy, we also offer an

empirical investigation to determine the role of socioeconomic, political and institutional factors

2 See the Economic Journal, vol. 109, no. 456, June 1999 the feature bControversy: on the hidden economyQ.3 The literature about the bshadowQ, bundergroundQ, binformalQ, bsecondQ, bcashQ or bparallelQ, economy is increasing.

Various topics, on how to measure it, its causes, and its effect on the official economy are analyzed. See for example,

survey type publications by Frey and Pommerehne (1984); Johnson et al. (1997, 1998); Lippert and Walker (1997);

Loayza (1996); Pozo (1996); Schneider (1994a,b, 1997, 1998, 2005) and Thomas (1992); and for an overall survey of the

global evidence of its size in terms of value added Schneider and Enste (2000, 2002).4 This definition is used for example, by Feige (1989, 1994), Frey and Pommerehne (1984), and Lubell (1991) and

Schneider (1994a, 2005).

K. Chaudhuri et al. / Journal of Development Economics 80 (2006) 428443 429


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explaining the size of the hidden economy. Our approach thus demonstrates the importance of

policy actions in increasing government responsiveness to curb the size of the hidden economy.

We particularly emphasize on the role of election, nature of the governments, literacy, mass

media, and the impact of liberalization in this context. Kaufman (1999) considers knowledge andinformation; leadership and collective action can be used as the prime weapons to tackle

corruption. Sta penhurst (2000) provides a brief link between an active media and the

amelioration of corruption as also the shadow activities of the economy. Djankov et al.

(2002) demonstrate that the government ownership of the media is generally associated with less

press freedom, fewer political and economic rights, and, most importantly, inferior social

outcomes in the areas of education and health. Ahrend (2002) provides strong empirical

evidence that suggests that strengthening press freedom should be among the priorities fighting

against corruption. Dyck and Zingales (2002) discuss the role of the media influencing corporate

policy.

India provides an interesting framework. India has been a democracy since 1947. Periodicelections to the national and state legislative assemblies have taken place since 1952. There is a

relatively free and independent press with significant time-series and cross-sectional variation.

Using these data, we are able to examine a connection between the development of mass media,

political and institutional factors and government actions to cater the needs of the citizens. In this

connection, our paper can be viewed in line with the growing literature that uses data from India

to examine the role of institutional and political factors to explain government responsiveness.

Besley and Burgess (2000) demonstrate that party ideology affects public policy: the cumulative

land reforms passed in a given state-year depend on the 4-year lagged state legislative assembly

seat shares of different political groups. Besley and Burgess (2002) show that state governments

are more responsive to falls in food production and crop damage in those states where newspapercirculation is higher and electoral accountability is greater. Banerjee and Iyer (2004) document

that differences in historical institutions lead to very different policy choices, and hence to

differences in economic outcomes. Iyer (2004) demonstrates that areas under direct British rule

have significantly lower levels of public goods in the present period. Hoff (2003) provides a

survey relating institutional developments and its impact on economic growth. However, none of

the above-mentioned papers has addressed the issue of the size of the unofficial economy in the

Indian state context. Our paper is an attempt in this direction.

The Wanchoo Committee Report (Government of India, Ministry of Finance, 1971, p. 6) was

the first to draw attention on the shadow economy in India. They referred to the phenomenon as

a bcancerous growth in the countrys economy which if not checked in time, will surely lead toits ruinationQ. The Venkatappiah Committee Report (Government of India, 1974), which focused

on the self-removal of excise taxes also feltbfree to confess that we are not prepared for, and are,

therefore, painfully surprised at, the range, diversity and, in certain segments of production,

almost the universality of the evasion, which is practiced by those who produce the goods Q.

Besides taxes, the extent of regulation present in the economy in the form of industrial licensing,

import licensing, controls on prices and distribution channels of goods and services, credit

controls and other measures can encourage the proliferation of the hidden economy. The Dagli

Committee Report (1979) highlighted this phenomenon.

The remaining parts of the paper are organized in the following manner. The next section

presents a brief review of the literature for estimation of the size of the hidden economy. Section

3 discusses the estimation methodology. Section 4 is divided in to three subsections: the first

subsection lists out the basic data variables used in the analysis, the second one document the

nature of the hidden economy estimates obtained by the used methodology, and the last deals

K. Chaudhuri et al. / Journal of Development Economics 80 (2006) 428443430


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with the role of political and institutional factors explaining the size of the hidden economy.

Section 5 concludes.

2. Methods of measuring the hidden economy

Our study is different from the earlier underground economy studies conducted in the Indian

context by Gupta and Mehta (1981), Chopra (1982), Acharya (1985), Bhattacharyya (1999), and

Bhattacharyya and Ghose (1998) in the following way:

1) While Acharya (1985), Bhattacharyya (1999), and Bhattacharyya and Ghose (1998) used

traditional cash demand estimation methodology, which has been criticized in the literature

for its focus on just one facet of the hidden economy, this work uses the MIMIC model.

Gupta and Mehta (1981) have used a physical input approach where as Chopras method is in

close line with the one suggested by Kaldor (1956).5

2) The uniqueness of the study hinges on the fact that it addresses the crucial question: Does an

increase in the presence of civic institutions like media have a contractionary effect on the

size of the hidden economy of a democracy like India?

In this paper, we attempt to estimate the hidden economy by multiple indicator multiple cause

approach (MIMIC). Frey and Week-Hannemann (1984) were the first to employ this

methodology for the estimation of the hidden economy of a cross-section of 17 OECD

countries for the period of 196078. They borrowed from the statistical theory of unobserved

variables developed by the likes of Zellner (1970), Goldberger (1972) and Joreskog and

Goldberger (1975) which considers multiple causes and multiple indicators of the phenomenonto be measured and used a factor-analytic approach to measure the hidden economy as an

unobserved variable over time. The unknown coefficients are estimated separately through a set

of structural equations with the indicator variables being used to capture the effect of the

unobserved variables indirectly. Frey and Week-Hannemann (1984) provided a ranking of

OECD countries based on the size of their underground economies. In the late 1970s,

Scandinavian and Benelux countries were seen to have very large hidden economies followed by

US in the middle rank and then by Switzerland and Japan, which exhibit very small sizes of

underground activity for that period. Also growth rates wise, Denmark, Belgium, and Italys

hidden segment seem to have grown at an above average pace while Canada, UK and USAs

hidden economy was found to be below the average rate. Another study by Aigner et al. (1988)uses a variant of the MIMIC approach the DYMIMIC (the dynamic multiple-indicators

multiple-causes approach) to assess the size of the US hidden economy for the period 1939

1982. The results of this study have found a peak in the US hidden economy size around 1943

44 and a trough in 196768.

In recent years, a lot of work has been done using the unobserved or latent variable approach,

particularly in the context of New Zealand and Canada. Giles (1999a,b), Giles and Caragata

(2001), and Giles and Tedds (2002) have used the time series data for the New Zealand and the

Canadian economy, to arrive at hidden economy estimates, using the MIMIC approach. To the

best of our knowledge, this is the first attempt to estimate the size of the hidden economy using

MIMIC model in the Indian context.

5 Kaldor (1956) tried to estimate the size of the hidden economy in India, by estimating the income that avoided the

income tax.



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3. Estimation methodology

This section describes in brief the MIMIC variable approach. The MIMIC model actually is a

variant of the LISREL (linear independent structural relationships) models of Joreskog andSorbom (1993a,b) and others that can only yield a time-series index for the latent variables: an

ordinal index. We need to convert ordinal index into a cardinal series of values of hidden

economy sizes by scaling up the ordinal values to some cardinal value that has been obtained in

the past through other methods of estimation like the electricity or the currency demand

approach. The MIMIC model equations can be stated as:

y kg e 1

g cVx f 2

where y is a column vector ofdp

Tindicators of the latent variable, g, and x is a column vector of

the dqT bcausesQ ofg. In other words, Eq. (1) is the measurement model forg and Eq. (2) is the

structural equation for the latent variable, g. e is a (px1) measurement error while f is the scalar

structural error. It is assumed that f and all the elements of e are mutually uncorrelated, with

var(f) =w, and cov(e) =He. Substituting (2) into (1), the MIMIC model can be expressed as a p-

equation multivariate regression model:

y Pxz 3

where P =kgV, z=kf+ e, and cov(z) =kkVw+He.

The p-equation model in (3) seems to have a regression matrix of rank equal to one and an

error covariance matrix that is similarly constrained. The first condition is typical insimultaneous equation models where the removal of a few exogenous variables from the

structural equation might cause a part of the reduced form coefficient matrix to be short-ranked.

The singularity property of the error covariance matrix develops because for the measurement

model to be estimated it has to be normalized first. This implies that the estimation of (1) and (2)

can be carried only after (1) is normalized by setting one element ofk to a pre-assigned value.

We estimate the model using the Maximum Likelihood Estimation procedure.

We use the following combinations of the causes and indicators to arrive at different hidden

economy estimates. For the indicators, we use the growth in real net state domestic product of

the Indian states (Grsdp)6 and the total number of employees (sum of productive and non-

productive workers) in registered manufacturing industries adjusted by the total number offactories in a state (Temp). For the causal variables, we have included the following: capital

account developmental expenditure (Capdev), capital account non-developmental expenditure

(Cqndev), states own tax revenue (Otr), states own non-tax revenue (Ontr), states current

account developmental expenditure (Curdev), and states current account non-developmental

expenditure (Curndev). All the expenditures and tax variables are expressed as a proportion of

states net domestic product.

The total revenue of a state government consists of two components: total tax revenue,

and total non-tax revenue. A state governments total tax revenue is, in turn, decomposed

into two parts: its share in the tax revenue of the central government and revenue raised

6 We have also tried using the growth in per capita real net state domestic product instead of growth in real net state

domestic product. Our results in terms of the size of the hidden economy remain almost the same. The results are

available on request.



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through state taxes. State taxes are mainly indirect in natures.7 A state governments non-tax

revenue derives from two sources: grants from the central government, and own non-tax

revenue. The interest receipts from loans issued by the state government, dividends and profits

from public sector undertakings owned by the state government, and revenues from statelotteries are the major constituents of the non-tax revenue. In this paper, we use the own tax

revenue and own non-tax revenue components of total revenue. The total expenditures incurred

by state governments are on either the current account or the capital account. Current account

expenditure is of three types: developmental spending, non-developmental spending and grants

to local governments. Developmental current account spending mainly meets the need to

maintain the existing assets mainly in terms of economic services (inclusive of expenditure on

agriculture, industry, power and irrigation, transport and communications) and social services

(inclusive of education, health and family welfare, planned expenditure on social security),

where as non-developmental part consists of interest payments on past debts, expenditure on

fiscal and administrative services, pension and retirement benefits, non-planned expenditure onsocial security and welfare and food subsidy. Capital account expenditure consists of two parts:

development and non-developmental where the former mainly concentrates on creation of

physical assets. Non-developmental part of the capital expenditure is mainly used for repayments

for loans to central governments and discharge of internal debt.

As stated earlier, the MIMIC model can only yield a time-series index for the latent variables

(the underground economy). However, it can only give an index for a time-series. Therefore, we

need to convert this ordinal index into a cardinal series of values of hidden economy sizes by

scaling up the ordinal values to some cardinal value obtained in the past through other methods

of estimation like the electricity or the currency demand approach and using values from it to

calibrate the ordinal series obtained by the MIMIC approach. Here we have adopted this option.We have used Bhattacharyyas (1999) hidden economy estimate for India, of 22.5% for 1989

90, to scale up our ordinal hidden economy series to arrive at the complete cardinal underground

economy sizes for different states for India.

Given the above estimates, in the next stage, we have tried to explain whether the civic

institutions like media, political institutions or characteristics of the state governments affect the

growth in the size of the hidden economy estimates (git). The regression equation for this model

takes the standard panel data form:

git ai ki qlog Hit1 lti beit dfit gsit /wit upit wlitit lprimaryit hrurit uit 5

where ai is a state fixed effect and kt is a time-dummy controlling for aggregate shock. The

term, Hit1 is the lagged size of the hidden economy. We have allowed for the state-specific

trend (ti) in our estimation. The variable fit variable represents the government characteristics

in terms of coalition measuring the proportion of year dtT where a coalition government is in

power with more than one pivotal party. We call this variable as Coalition government. The

variable eit represents an election year dummy taking the value of one if a scheduled election

is held in the second half of financial year t or in the first half of the next financial year in

state i. We have also included two other variables (sit and wit) in the above equation. The firstone captures the extent of political affiliation between the governments at the center and the

7 On an average, this amounts to around 8788% of total state tax revenue.



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state. Specifically, this is a dummy variable that takes the value of 1(0) if the government in

state s is politically affiliated with the central government for more (less) than 6 months

during financial year t. The second one tries to capture the difference between a left-wing state

government (that is, a government headed by a communist party) and all other governmenttypes. The variable wit measures the proportion of financial year t during which the

government of state i was a left-wing government. We have also introduced two other

variables in Eq. (5): namely the proportion of rural population (Rur), and contribution of

primary sector (primary) (agriculture and allied services) in total net state domestic product. The

variable denoted by pit refers to the growth in per capita total newspaper circulation while lit

refers to the growth in literacy rates.

In Eq. (5), the error term uit is modeled as an AR(1) process where we allow state-specific

degree of autocorrelation. Estimation of Eq. (5) via generalized least squares also permits us for

a heteroskedastic error structure with each state having its own variance.8

4. Empirical results

4.1. Data

The data set for our study consists of annual observations from 197475 to 199596

covering the 14 major states of India. The fourteen major Indian states are: Andhra Pradesh,

Bihar, Gujarat, Haryana, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Orissa, Punjab,

Rajasthan, Tamil Nadu, Uttar Pradesh and West Bengal. The variables that we consider

partition into two major categories: (1) data on the indicator variables (Grsdp and Temp)

and (2) data on cause variables. The real net state domestic product is obtained fromvarious issues of the National Accounts Statistics (Government of India, Ministry of

Planning, Department of Statistics), published by the Central Statistical Organization of the

Government of India. The employment data for the registered manufacturing industries were

complied from various issues of the Annual Survey of Indian Industries, Ministry of

Statistics and Program Implementation, Government of India. The data on cause variables

such as tax and expenditure variables of state governments were collected from various

volumes of the Reserve Bank of India Bulletin, published by the Central bank of India.

State demographic characteristic like the contribution of the primary sector is constructed

using the National Accounts Statistics. The literacy rates are collected from Census of India

and the National Sample Survey rounds (both published by the Government of India). Forsome years, state literacy rate data were not available from either the Census of India or the

National Sample Survey rounds. For these years, we have interpolated the data using a simple

growth rate formula. We obtain the per capita newspaper circulation figures from Press in

India (published by the Ministry of Information and Broadcasting). We have also experiment

with total vernacular language newspaper circulation, total English newspaper circulation, total

second language newspaper circulation and total other language newspaper circulation. All

these come from Press in India. Our data on political variables comes from multiple sources.

First, the dates of all state legislative assembly elections were taken from the book India

Decides (Butler et al., 1996) and from the official website of Election Commission of India.9

8 Besley and Burgess (2000) have also followed this kind of estimation strategy.9 http://www.eci.gov.in.

http://www.eci.gov.in/http://www.eci.gov.in/http://www.eci.gov.in/


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Thereafter, for each state-year combination, the bnatureQ of the state government (coalition,

political affiliation and ideology) was determined. We have collected this information from the

publication Encyclopedia of India and Her States (Grover and Arora, 1998).

4.2. Results

This section is divided in two parts: Section 4.2.1 reports the results for the MIMIC model

where as the results from the estimation of Eq. (5) is reported in Section 4.2.2.

Table 1

MIMIC model results

Variables State of

Andhra Pradesh Kerala Maharashtra West Bengal

IndicatorsGrsdp 0.04 (0.25) 0.47 (2.10)* 0.13 ( 0.51) 0.20 (0.62)

Annual growth of real net state

domestic product of Indian states

Temp 1.00 1.00 1.00 1.00

Total number of employees in

registered manufacturing industries

adjusted by total number of

factories in a state

Causes

Capdev 0.44 (3.82)*** 0.03 (0.32) 0.03 (0.23) 0.10 (0.68)

Capital account developed mental

expenditures in percent of states

net domestic product

Capndev 0.25 ( 2.07)* 0.05 (0.46) 0.02 (0.06) 0.18 (0.62)Capital account non developed

mental expenditures in percent

of states net domestic product

Otr 0.13 ( 0.60) 0.88 (3.52)*** 0.60 ( 1.80)* 0.42 (1.12)States own tax revenue in percent

of states net domestic product

Ontr 0.15 ( 1.24) 0.35 (3.44)*** 0.22 (0.75) 0.10 (0.34)

States own non tax revenue in percent of states net domestic

product

Curdev 0.20 ( 0.79) 0.20 (1.21) 0.16 ( 0.35) 0.78 (2.68)**States current account

development expenditure in

percent of states net

domestic product

Curndev 0.43 ( 3.56) *** 0.31 (1.19) 0.17 ( 0.86) 0.69 (1.79)*States current account non

development expenditure in

percent of states net

domestic product

t-Statistics are reported in parenthesis.

* Denotes significance at 10% level.

** Denotes significance at 5% level.

*** Denotes significance at 1% level.



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4.2.1. The MIMIC models estimates of the hidden economy of Indian states

In this sub-section, we present the hidden economy estimates of the Indian states. We have

estimated the MIMIC model separately for individual states.10 For an illustrative purpose,

however in Table 1 we present the results for four states, namely, Andhra Pradesh, Kerala,

Maharashtra and West Bengal. Our results clearly indicate the following: although the causal

variables enter in general with expected signs, however, many of them lack individual

significance. Table 2 reports the diagnostic statistics for the estimated model. Small values ofroot mean square residual (RMR) where as large values of the adjusted goodness of fit index

(AGFI), and the parsimony goodness of fit index (PGFI), reflect a good model fit. In almost all

the models, the diagnostic statistics give a satisfactory result in terms of model fit. In Table 3, we

present the results for the size of the underground economy of Indian states for four samples:

1974/751980/81, 1981/821985/86, 1986/871991/92 and 1992/931995/96. Table 3 depicts

some interesting pictures: for the entire period, state of Haryana has the lowest size of

underground economy followed closely by the southern state of Tamil Nadu while that of Bihar

is the highest. The southern states namely, Andhra Pradesh, Karnataka, Kerala and Tamil Nadu

represent lower underground economy in comparison with other states of India. Three of five

BIMARU11 states have an average size of under-ground economy that is larger than that of all-14 average. States like Haryana, Orissa, Punjab, Rajasthan, Uttar Pradesh and West Bengal show

a considerable size of the hidden economy in the post-liberalization era (1992/931995/96)

compared to the entire period.

In Fig. 1, we also provide a diagrammatic exposition of the average size of the hidden

economy for fourteen major states along with that of all-India.12 In this figure, the column all-14

represents a simple average for the fourteen major states. For the sample period, the hidden

economy for all-India stands at 20.35% of GDP. The size of the hidden economy has increased

10 Although we do not provide the estimates for each one of them, the results, however, are available for the authors on

request.11 The BIMARU states are Bihar, Madhya Pradesh, Orissa, Rajasthan, and Uttar Pradesh.12 For the estimation of the hidden economy at the all-India level, please see Chattopadhyay et al. (2003).

Table 2

Diagnostic statistics of the estimated MIMIC model

State Root mean square

residual (RMR)

Adjusted goodness of

fit index (AGFI)

Parsimony goodness

of fit index (PGFI)

Andhra Pradesh 0.046 0.66 0.13

Bihar 0.110 0.53 0.13

Gujarat 0.051 0.87 0.14

Haryana 0.087 0.37 0.13

Karnataka 0.050 0.80 0.14

Kerala 0.074 0.23 0.12

Madhya Pradesh 0.150 0.26 0.12

Maharashtra 0.100 0.61 0.13

Orissa 0.046 0.63 0.13

Punjab 0.020 0.91 0.14

Rajasthan 0.040 0.92 0.14

Tamil Nadu 0.070 0.27 0.12Uttar Pradesh 0.092 0.26 0.12

West Bengal 0.066 0.37 0.12



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from 15.39% (1974/75) to 2321% (1995/96). States as Bihar, Gujarat, Madhya Pradesh,

Maharashtra, Punjab, and Rajasthan had experienced a higher size on an average compared to

all-India figure.

A comparison of the estimated size of the hidden economy for the all-India along with someother Asian countries (reported in Appendix A)13 reveals that Thailand has by far, for the year

1994/95, the biggest shadow economy with 48.3% of the official GDP, followed by the

Philippines with 38.4% and Sri Lanka with 35.3%. In the middle field is Taiwan with 17.4%,

India with 20.3% and South Korea with 22.4%. At the lower end is China with 10.2%, Japan

with 10.6% and Singapore with 11.2% of the bofficialQ GDP.

In order to get a better understanding, we also calculate the growth rate of the underground

economy for the pre-liberalization (1975/761991/92) and post-liberalization (1992/931995/

96) for Indian states and report the results in Table 4. In the post-liberalization era, the growth in

the size of the underground is lower is most states except Haryana, Kerala, Rajasthan and West

Bengal. The state of Rajasthan had the highest average growth rate in the post-liberalization period whereas Andhra Pradesh had the lowest one. In the pre-liberalization era, Kerala

experienced the lowest average growth rate where as that of Orissa was the highest.

4.2.2. Explaining the size of the hidden economy of Indian states

In order to explain the size of the hidden economy, we ran the regression as given in Eq. (5).

The result is reported in Table 5. Column 1 of Table 5 presents the regression results where we

just used the political variables, namely the scheduled election, the coalition government, the

match dummy, ideology of the government and the liberalization dummy. The lagged value of

Table 3

Size of the hidden economy for states of India as a percent of measured net state domestic product

State Time period

1974/751980/81 1981/821986/87 1987/881991/92 1992/931995/96 1974/751995/96

Mean S.D. Mean S.D. Mean S.D. Mean S.D. Mean S.D.

Andhra Pradesh 14.437 2.357 20.235 3.198 22.177 0.711 18.724 2.399 18.557 3.811

Bihar 22.642 2.847 26.623 4.586 25.954 2.520 27.422 1.731 25.350 3.573

Gujarat 16.278 2.574 20.957 2.589 25.160 4.481 21.011 1.493 20.433 4.341

Haryana 9.308 2.664 15.937 2.397 21.114 1.014 28.400 4.836 17.148 7.503

Karnataka 16.419 3.537 22.150 3.363 22.965 1.809 19.739 1.063 20.073 3.840

Kerala 15.638 1.958 19.917 2.563 21.788 0.812 22.531 1.236 19.456 3.319

Madhya Pradesh 22.071 6.700 25.687 1.744 24.708 2.997 23.038 2.630 23.832 4.309

Maharashtra 17.047 1.168 22.284 1.566 23.181 0.838 23.392 1.881 21.023 3.084

Orissa 9.971 2.825 15.107 4.865 24.019 2.474 29.715 1.490 18.154 8.247

Punjab 15.286 1.806 20.419 1.760 24.808 1.694 27.598 0.714 21.088 5.001Rajasthan 17.003 3.823 19.862 3.425 24.586 2.800 31.584 6.052 22.157 6.510

Tamil Nadu 15.839 1.526 21.430 1.086 23.119 2.489 22.268 1.604 20.187 3.479

Uttar Pradesh 11.285 1.047 14.395 1.884 21.625 1.941 27.016 1.531 17.343 6.254

West Bengal 16.907 2.428 16.012 3.951 22.103 2.517 29.812 3.232 20.190 5.931

Average (all 14) 15.724 4.628 20.041 4.552 23.379 2.545 25.161 4.642 20.357 5.555

India 17.060 1.128 20.377 0.926 22.475 0.428 23.399 0.310 20.348 2.659

13 The calculation of the shadow economy for China is very difficult and the values may be questioned because only a

part of China has so far been developed into a market economy. A great part of China still belongs to a planed economy

and due to this mix of systems, the calculated figures may not be very reliable.



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the hidden economy is negative and significant implying the evidence of convergence. Our

results show that the growth in the size of the hidden economy is approximately 5% less in

scheduled election years than in all other years. The coefficient associated with the Coalition

Government is negative and statistically significant. The estimates reveal that the growth in the

size of the hidden economy is around 3% less if the coalition is in power compared to a single-

party government. The trend term is significant implying that the hidden economy is growing for

the Indian states significantly. We also obtain the fact that the state where the left-wing

government (Ideology) is in power the growth in the size of the hidden economy is less. The

liberalization of the Indian economy that took place in 1991/92 also exerts a negative significant

impact.In order to examine the robustness of the results reported in Column 1, in column 2, we

introduced the following variable as a determinant of the growth in the Indian economy: the

contribution of the primary sector (agriculture and allied services) in net state domestic product

and proportion of rural population as control variables. Our results in terms of the political

0

5

10

15

20

25

30

Andh

raPra

desh

Bihar

Gujar

at

Harya

na

Karna

takaKe

rala

Madh

yaPrad

esh

Mahar

ashtra

Oriss

aPu

njab

Rajas

than

Tami

lNadu

Uttar

Prad

esh

WestBe

ngal

All-

14Ind

ia

State

In%o

fOfficialSDP(GDP)

Fig. 1. Average size (1974/751995/96) of the hidden economy of 14 Indian states.

Table 4

Growth in size of the hidden economy for states of India

State Pre-liberalization (1975/761991/92) Post-liberalization (1992/931995/96)

Mean S.D. Mean S.D.

Andhra Pradesh 0.049 0.099 0.067 0.059Bihar 0.046 0.199 0.014 0.070Gujarat 0.047 0.202 0.068 0.139Haryana 0.079 0.313 0.122 0.123

Karnataka 0.056 0.156 0.040 0.075Kerala 0.025 0.094 0.036 0.027

Madhya Pradesh 0.047 0.185 0.007 0.186

Maharashtra 0.026 0.054 0.018 0.101

Orissa 0.163 0.508 0.019 0.059

Punjab 0.045 0.097 0.013 0.014

Rajasthan 0.049 0.202 0.117 0.105

Tamil Nadu 0.041 0.078 0.051 0.077Uttar Pradesh 0.058 0.076 0.056 0.067

West Bengal 0.056 0.259 0.090 0.085

All 14 0.056 0.211 0.017 0.103

India 0.024 0.010 0.016 0.027



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Table 5

Regression results: growth in the size of the hidden economy

(1) (2) (3)

Lagged value of hidden economy 0.480 ( 10.1 l)*** 0.504 (11.40)*** 0.49Scheduled election dummy 0.052 ( 4.72)*** 0.049 (5.87)*** 0.04Trend 0.016 (6.13)*** 0.009 (3.36)*** 0.009

Coalition government 0.028 ( 2.61)*** 0.031 (3.60)*** 0.02Match dummy 0.004 (0.43) 0.016 (2.13)** 0.016

Ideology 0.024 ( 0.92) 0.054 (2.34)** 0.06Liberalization dummy 0.038 ( 2.94)*** 0.023 (1.99)** 0.02Contribution of the primary sector 1.072 (7.13)*** 1.03

Proportion of rural population 0.443 (0.93) 0.584

Growth in per capita total newspaper circulation 0.03Growth in per capita vernacular newspaper circulation

Growth in literacy rates 0.81Constant 1.328 (10.58)*** 1.561 (4.16)*** 1.443

State dummies Yes Yes Yes

Year dummies Yes Yes Yes

Observations 294 294 294

Numbers in parentheses are the calculated Z-statistics.*Denotes significance at 10% level. **Denotes significance at 5% level. ***Denotes significance at 1% level.


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variable (the election dummy, coalition government, match dummy and ideology of the

government) remain the same except the fact that both the match dummy and ideology becomes

significant. Our result infers that state that has the same political party in power as in the Center

is less active in terms of curbing the growth in the size of the hidden economy. We also note thatan increase in the contribution of the primary sector significantly reduces the growth in the size

of the hidden economy.

Next, we try to focus on the role of print-media. In column 3 Table 5 we have introduced both

the growth in literacy rates and that in per capita total newspaper circulation. The reported results

document that both the variables yield the expected negative sign, although the effect is

significant only in case of growth in per capita total newspaper circulation. This shows that state

governments are more responsive to reduce the size of the hidden economy in those states where

newspaper circulation is higher. In column (4) of Table 5, we have used growth in per capita

newspaper circulation published in vernacular language instead of per capita total newspaper

circulation. The coefficient associated with the variable is negative, although not significant. Ourresults almost remain the same with respect to other variables.

We have used the scheduled election years only. However, if the regressions are re-estimated

without differentiating between scheduled and mid-term elections, our reported results in Table 5

remains the same with the election variable enters with a negative and significant coefficient.

Second, the effects of government fragmentation are robust and do not depend on how the

election year dummy is coded. Third, the coefficient associated with the match dummy variable

remains unaltered.14 However, the ideology variable loses its significance.

Given these results, we ran another regression where we include the mid-term election as a

separate variable along with the scheduled election to differentiate the impact of these two

different types of elections. Here we obtain that both the scheduled and the mid-term electionenters with negative significant coefficient.15 A test for the difference in estimated coefficients

associated with these two variables reveals significant difference in the estimated coefficients at

the 10 percent level. The results in terms of the other variable remain qualitatively the same.16

In sum, we can infer that the growth in the size of the hidden economy is significantly lower

in election years than all other years. A state where the coalition government is in power also

experiences lower increase in the growth compared to single-party governments. Our results also

provide some weak evidence that an increased growth in literacy rates and newspaper circulation

results in a lower growth rate in the size of the hidden economy. We also document that

increased competition in terms of liberalization of the Indian economy in 1991 also helps to

reduce the growth in the size of the underground economy.

5. Conclusion

This paper tries to estimate the size of the hidden economy using state level data from India

over the period 1974/75 to 1995/96. We have used a MIMIC model. The estimates from the

MIMIC model demonstrate the varying size of the hidden economy in Indian states. On an

average, the size has grown from 13.1% to 26.3%.

14 The coefficient is 0.041 with a p-value of 0.000.15 The coefficient associated with the scheduled election is 0.048 and that of mid-term election is 0.028. The test for

the difference in the estimated coefficients associated with these two variables yields a test statistic of 2.79 distributed as

v2 with one degree of freedom. This is significant at the 10% level.16 The detailed results are available on request.



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We have also shown that an increased growth of per capita newspaper circulation helps

to curb the growth in the size of the shadow economy activities in the economy. This focuses

on the importance of free and independent regional presses as key factors for proper

functioning of the democracy. Our result also demonstrates that the state governments areactive during the election to provide a cleaner picture of the economy. Elections act as an

incentive for politicians to perform. The growth in the size of the hidden economy is lower if

the coalition government is in power. Our result also provides evidence in favor of

liberalization of the Indian economy in order to reduce the growth in the size of the hidden

economy.

Appendix A

Size of the hidden economy for some Asian countries (percent of official GDP in 1994/95)

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